homework
amane
2022/5/9
library(leaflet) #パッケージの読み込み
popup = c("Robin", "Jakub", "Jannes")
leaflet() %>%
addProviderTiles("NASAGIBS.ViirsEarthAtNight2012") %>% #表示する地図の種類を選択
addMarkers(lng = c(-3, 23, 11), #経度設定
lat = c(52, 53, 49), #緯度設定
popup = popup)library(sf) #ベクトルデータ## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1library(raster) #ラスターデータ## Loading required package: splibrary(spData) #地図データ
library(spDataLarge)world #spDataに含まれているデータセット## Simple feature collection with 177 features and 10 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## Geodetic CRS: WGS 84
## # A tibble: 177 x 11
## iso_a2 name_long continent region_un subregion type area_km2 pop lifeExp
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 FJ Fiji Oceania Oceania Melanesia Sove… 1.93e4 8.86e5 70.0
## 2 TZ Tanzania Africa Africa Eastern … Sove… 9.33e5 5.22e7 64.2
## 3 EH Western … Africa Africa Northern… Inde… 9.63e4 NA NA
## 4 CA Canada North Am… Americas Northern… Sove… 1.00e7 3.55e7 82.0
## 5 US United S… North Am… Americas Northern… Coun… 9.51e6 3.19e8 78.8
## 6 KZ Kazakhst… Asia Asia Central … Sove… 2.73e6 1.73e7 71.6
## 7 UZ Uzbekist… Asia Asia Central … Sove… 4.61e5 3.08e7 71.0
## 8 PG Papua Ne… Oceania Oceania Melanesia Sove… 4.65e5 7.76e6 65.2
## 9 ID Indonesia Asia Asia South-Ea… Sove… 1.82e6 2.55e8 68.9
## 10 AR Argentina South Am… Americas South Am… Sove… 2.78e6 4.30e7 76.3
## # … with 167 more rows, and 2 more variables: gdpPercap <dbl>,
## # geom <MULTIPOLYGON [°]>plot(world)## Warning: plotting the first 9 out of 10 attributes; use max.plot = 10 to plot
## all
world["lifeExp"] #lifeExpの配列を選択## Simple feature collection with 177 features and 1 field
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## Geodetic CRS: WGS 84
## # A tibble: 177 x 2
## lifeExp geom
## <dbl> <MULTIPOLYGON [°]>
## 1 70.0 (((180 -16.06713, 180 -16.55522, 179.3641 -16.80135, 178.7251 -17.01…
## 2 64.2 (((33.90371 -0.95, 34.07262 -1.05982, 37.69869 -3.09699, 37.7669 -3.…
## 3 NA (((-8.66559 27.65643, -8.665124 27.58948, -8.6844 27.39574, -8.68729…
## 4 82.0 (((-122.84 49, -122.9742 49.00254, -124.9102 49.98456, -125.6246 50.…
## 5 78.8 (((-122.84 49, -120 49, -117.0312 49, -116.0482 49, -113 49, -110.05…
## 6 71.6 (((87.35997 49.21498, 86.59878 48.54918, 85.76823 48.45575, 85.72048…
## 7 71.0 (((55.96819 41.30864, 55.92892 44.99586, 58.50313 45.5868, 58.68999 …
## 8 65.2 (((141.0002 -2.600151, 142.7352 -3.289153, 144.584 -3.861418, 145.27…
## 9 68.9 (((141.0002 -2.600151, 141.0171 -5.859022, 141.0339 -9.117893, 140.1…
## 10 76.3 (((-68.63401 -52.63637, -68.25 -53.1, -67.75 -53.85, -66.45 -54.45, …
## # … with 167 more rowsworld %>% dplyr::select(lifeExp) #上と同じ## Simple feature collection with 177 features and 1 field
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## Geodetic CRS: WGS 84
## # A tibble: 177 x 2
## lifeExp geom
## <dbl> <MULTIPOLYGON [°]>
## 1 70.0 (((180 -16.06713, 180 -16.55522, 179.3641 -16.80135, 178.7251 -17.01…
## 2 64.2 (((33.90371 -0.95, 34.07262 -1.05982, 37.69869 -3.09699, 37.7669 -3.…
## 3 NA (((-8.66559 27.65643, -8.665124 27.58948, -8.6844 27.39574, -8.68729…
## 4 82.0 (((-122.84 49, -122.9742 49.00254, -124.9102 49.98456, -125.6246 50.…
## 5 78.8 (((-122.84 49, -120 49, -117.0312 49, -116.0482 49, -113 49, -110.05…
## 6 71.6 (((87.35997 49.21498, 86.59878 48.54918, 85.76823 48.45575, 85.72048…
## 7 71.0 (((55.96819 41.30864, 55.92892 44.99586, 58.50313 45.5868, 58.68999 …
## 8 65.2 (((141.0002 -2.600151, 142.7352 -3.289153, 144.584 -3.861418, 145.27…
## 9 68.9 (((141.0002 -2.600151, 141.0171 -5.859022, 141.0339 -9.117893, 140.1…
## 10 76.3 (((-68.63401 -52.63637, -68.25 -53.1, -67.75 -53.85, -66.45 -54.45, …
## # … with 167 more rowsworld %>% dplyr::select(lifeExp) %>% st_drop_geometry() #lifeExpの中身だけを表示## # A tibble: 177 x 1
## lifeExp
## * <dbl>
## 1 70.0
## 2 64.2
## 3 NA
## 4 82.0
## 5 78.8
## 6 71.6
## 7 71.0
## 8 65.2
## 9 68.9
## 10 76.3
## # … with 167 more rowsst_drop_geometry(world["lifeExp"]) #上と同じ## # A tibble: 177 x 1
## lifeExp
## * <dbl>
## 1 70.0
## 2 64.2
## 3 NA
## 4 82.0
## 5 78.8
## 6 71.6
## 7 71.0
## 8 65.2
## 9 68.9
## 10 76.3
## # … with 167 more rowsclass(world)## [1] "sf" "tbl_df" "tbl" "data.frame"plot(world["lifeExp"]) #geometryで表示
world_asia = world[world$continent == "Asia", ] #Asiaだけを選択
asia = st_union(world_asia) #Asiaの形を切り取り## although coordinates are longitude/latitude, st_union assumes that they are planar#> although coordinates are longitude/latitude, st_union assumes that they are planar
world_asia## Simple feature collection with 47 features and 10 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 26.04335 ymin: -10.35999 xmax: 145.5431 ymax: 55.38525
## Geodetic CRS: WGS 84
## # A tibble: 47 x 11
## iso_a2 name_long continent region_un subregion type area_km2 pop lifeExp
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 KZ Kazakhstan Asia Asia Central … Sove… 2729811. 1.73e7 71.6
## 2 UZ Uzbekistan Asia Asia Central … Sove… 461410. 3.08e7 71.0
## 3 ID Indonesia Asia Asia South-Ea… Sove… 1819251. 2.55e8 68.9
## 4 TL Timor-Les… Asia Asia South-Ea… Sove… 14715. 1.21e6 68.3
## 5 IL Israel Asia Asia Western … Coun… 22991. 8.22e6 82.2
## 6 LB Lebanon Asia Asia Western … Sove… 10099. 5.60e6 79.2
## 7 PS Palestine Asia Asia Western … Disp… 5037. 4.29e6 73.1
## 8 JO Jordan Asia Asia Western … Sove… 89207. 8.81e6 74.0
## 9 AE United Ar… Asia Asia Western … Sove… 79881. 9.07e6 76.9
## 10 QA Qatar Asia Asia Western … Sove… 11328. 2.37e6 77.9
## # … with 37 more rows, and 2 more variables: gdpPercap <dbl>,
## # geom <MULTIPOLYGON [°]>asia## Geometry set for 1 feature
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 26.04335 ymin: -10.35999 xmax: 145.5431 ymax: 55.38525
## Geodetic CRS: WGS 84## MULTIPOLYGON (((120.295 -10.25865, 118.9678 -9....plot(world_asia)## Warning: plotting the first 9 out of 10 attributes; use max.plot = 10 to plot
## all
plot(asia)
plot(world["pop"], reset = FALSE) #worldのpopを地図にプロット
plot(asia, add = TRUE, col="red") #Asiaだけを赤でプロット
multipoint_matrix = rbind(c(5, 2), c(1, 3), c(3, 4), c(3, 2)) #位置を行列にして表示
multi_point <- st_multipoint(multipoint_matrix)
multipoint_matrix## [,1] [,2]
## [1,] 5 2
## [2,] 1 3
## [3,] 3 4
## [4,] 3 2class(multi_point)## [1] "XY" "MULTIPOINT" "sfg"multi_point## MULTIPOINT ((5 2), (1 3), (3 4), (3 2))lnd_point = st_point(c(0.1, 51.5)) #ロンドンの座標
class(lnd_point)## [1] "XY" "POINT" "sfg"lnd_geom = st_sfc(lnd_point, crs = 4326) #crs = coordinate reference system
lnd_attrib = data.frame( # data.frame object
name = "London",
temperature = 25,
date = as.Date("2017-06-21")
)
lnd_attrib## name temperature date
## 1 London 25 2017-06-21lnd_sf = st_sf(lnd_attrib, geometry = lnd_geom) # sf object
lnd_sf## Simple feature collection with 1 feature and 3 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 0.1 ymin: 51.5 xmax: 0.1 ymax: 51.5
## Geodetic CRS: WGS 84
## name temperature date geometry
## 1 London 25 2017-06-21 POINT (0.1 51.5)raster_filepath = system.file("raster/srtm.tif", package = "spDataLarge")
new_raster = raster(raster_filepath) #ラスターレイヤーを作成
new_raster## class : RasterLayer
## dimensions : 457, 465, 212505 (nrow, ncol, ncell)
## resolution : 0.0008333333, 0.0008333333 (x, y)
## extent : -113.2396, -112.8521, 37.13208, 37.51292 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## source : /usr/local/lib/R/site-library/spDataLarge/raster/srtm.tif
## names : srtm
## values : 1024, 2892 (min, max)plot(new_raster)
multi_raster_file = system.file("raster/landsat.tif", package = "spDataLarge")
multi_raster_file## [1] "/usr/local/lib/R/site-library/spDataLarge/raster/landsat.tif"r_brick = brick(multi_raster_file) #複数のラスターレイヤーを作成
r_brick## class : RasterBrick
## dimensions : 1428, 1128, 1610784, 4 (nrow, ncol, ncell, nlayers)
## resolution : 30, 30 (x, y)
## extent : 301905, 335745, 4111245, 4154085 (xmin, xmax, ymin, ymax)
## crs : +proj=utm +zone=12 +datum=WGS84 +units=m +no_defs
## source : /usr/local/lib/R/site-library/spDataLarge/raster/landsat.tif
## names : landsat.1, landsat.2, landsat.3, landsat.4
## min values : 7550, 6404, 5678, 5252
## max values : 19071, 22051, 25780, 31961plot(r_brick)
ndvi = (r_brick[[4]] - r_brick[[3]])/(r_brick[[4]] + r_brick[[3]]) #正規化植生指数
plot(ndvi)
library(sf)
library(raster)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:raster':
##
## intersect, select, union## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(stringr) # for working with strings (pattern matching)
library(tidyr) # for unite() and separate()##
## Attaching package: 'tidyr'## The following object is masked from 'package:raster':
##
## extractsel_area = world$area_km2 < 10000 #10000km2未満のデータを抽出
summary(sel_area) # a logical vector## Mode FALSE TRUE
## logical 170 7#> Mode FALSE TRUE
#> logical 170 7
small_countries = world[sel_area, ]
small_countries## Simple feature collection with 7 features and 10 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -67.24243 ymin: -16.59785 xmax: 167.8449 ymax: 50.12805
## Geodetic CRS: WGS 84
## # A tibble: 7 x 11
## iso_a2 name_long continent region_un subregion type area_km2 pop lifeExp
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 PR Puerto Ri… North Am… Americas Caribbean Depe… 9225. 3534874 79.4
## 2 PS Palestine Asia Asia Western … Disp… 5037. 4294682 73.1
## 3 VU Vanuatu Oceania Oceania Melanesia Sove… 7490. 258850 71.7
## 4 LU Luxembourg Europe Europe Western … Sove… 2417. 556319 82.2
## 5 <NA> Northern … Asia Asia Western … Sove… 3786. NA NA
## 6 CY Cyprus Asia Asia Western … Sove… 6207. 1152309 80.2
## 7 TT Trinidad … North Am… Americas Caribbean Sove… 7738. 1354493 70.4
## # … with 2 more variables: gdpPercap <dbl>, geom <MULTIPOLYGON [°]>plot(small_countries)
plot(small_countries["pop"]) #popだけプロット
world %>% filter(area_km2 < 10000) %>% dplyr::select(pop) %>% plot() #上までのプロットと同じ##GDPperCapitalがtop10の国をピックアップして地図化してみましょう。
world %>% top_n(n = 10, wt = gdpPercap) %>%
dplyr::select(gdpPercap) %>%
plot()
##GDPperCapitalがworst10の国をピックアップして地図化してみましょう。
world %>% top_n(n = -10, wt = gdpPercap) %>% #nを-にすると下から数えられる
dplyr::select(gdpPercap) %>%
plot()
##日本はGDPperCapitalで上から何番目でしょう。
#1.gdpPercapをsort
gdp_sort <- world %>% arrange(desc(gdpPercap)) %>% #arrangeで昇順にsort。descで降順
dplyr::select(gdpPercap) %>%
st_drop_geometry()
#2.日本のgdpPercapを取り出したい
jp_gdp <- world %>% filter(name_long == "Japan") %>%
dplyr::select(gdpPercap) %>%
st_drop_geometry() %>%
as.numeric()
#3.gdpPercapが上からいくつか調べればいい
which(gdp_sort==jp_gdp)## [1] 22R Markdown
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